Supply Chain Optimisation of Nipa-based bioethanol industry in Thailand

Abstract

Environmental concerns on greenhouse gases (GHG) and energy security are two of the main drivers of Thailand’s national energy policy mandating the bioenergy roles within their national energy portfolio. Currently approximately 60% of the oil consumption in Thailand is dependent on imports which is projected to rise to 95% by 2035 (IEA, 2013). Being one of the most GHG-intensive economies in South East Asia (IEA, 2013), the Thai authorities have announced in 2012 their Alternative Energy Development Plan including biofuel directive (Haema, 2012), setting ambitious energy and biofuel targets (9 ML bioethanol/day by 2021). This study presents a multi-objective optimization modelling framework configured to account for the economic and environmental impacts of a supply chain (SC) based on a mangrove-type feedstock, the Nipa palm. Nipa plantations are very demanding with respect to cultivation conditions thus constrained to coastline locations which need to be taken into account in SC design. To tackle this issue, a spatially-explicit multi-period mixed integer linear programming model was developed from scratch using the AIMMS platform in order to achieve optimal design of the biofuel SC at the cultivation, infrastructure, operation and transportation stages. The Nipa plantation, bioethanol production data was derived from the past and on-going research projects carried out at PSU as well as the primary inventory collected from field investigations. The model is capable of providing optimal configurations for a given bioenergy SC design, as well as providing an adjustable framework to address policy impacts at a large scale. The model functionality is demonstrated via a Thai Nipa-derived biofuel case study with a current national bioethanol demand and 2021 production target of 1.2 ML/day and 9 ML/day respectively. The Nipa feedstock is chosen to fulfil a fraction of this target demand. The carbon prices assumed in the case study for the 2010s, 2020s, 2030s are 20 £/tCO2, 25 £/tCO2 then 30 £/tCO2 respectively. The total area allocated to Nipa plantation for 2010-2030 varies between 1200 and 8800 ha. The total decadal SC costs are dominated by bioethanol production (79%). Nipa plantation, infrastructure, and transport stages overall contribute to 21% of total SC costs. It gives valuable insights on the implementation of the SC of a new alternative crop, which is required to support the development of advanced biofuels towards a more sustainable energy market.